import argparse
import json
import logging
import threading
import time
from contextlib import nullcontext
from copy import deepcopy
from datetime import datetime as dt
from pathlib import Path
from typing import Callable

import einops
import gymnasium as gym
import numpy as np
import torch
from huggingface_hub import snapshot_download
from huggingface_hub.utils._errors import RepositoryNotFoundError
from huggingface_hub.utils._validators import HFValidationError
from torch import Tensor, nn
from tqdm import trange

from lerobot.common.datasets.factory import make_dataset
from lerobot.common.envs.factory import make_env
from lerobot.common.envs.utils import preprocess_observation
from lerobot.common.logger import log_output_dir
from lerobot.common.policies.factory import make_policy
from lerobot.common.policies.policy_protocol import Policy
from lerobot.common.policies.utils import get_device_from_parameters
from lerobot.common.utils.io_utils import write_video
from lerobot.common.utils.utils import get_safe_torch_device, init_hydra_config, init_logging, set_global_seed
from lerobot.common.samplers.single import coherence_sampler, random_sampler
from lerobot.common.samplers.multi import contrastive_sampler, bidirectional_sampler

def rollout(
    env: gym.vector.VectorEnv,
    policy: Policy,
    seeds: list[int] | None = None,
    return_observations: bool = False,
    render_callback: Callable[[gym.vector.VectorEnv], None] | None = None,
    enable_progbar: bool = False,
    sampler: str = "coherence", 
    ah_test: int = 1, 
    reference_policy: Policy | None = None,
    temperature: float = 1.0,
    noise_level: float = 0.0
) -> dict:
    """Run a batched policy rollout once through a batch of environments.

    Note that all environments in the batch are run until the last environment is done. This means some
    data will probably need to be discarded (for environments that aren't the first one to be done).

    The return dictionary contains:
        (optional) "observation": A a dictionary of (batch, sequence + 1, *) tensors mapped to observation
            keys. NOTE the that this has an extra sequence element relative to the other keys in the
            dictionary. This is because an extra observation is included for after the environment is
            terminated or truncated.
        "action": A (batch, sequence, action_dim) tensor of actions applied based on the observations (not
            including the last observations).
        "reward": A (batch, sequence) tensor of rewards received for applying the actions.
        "success": A (batch, sequence) tensor of success conditions (the only time this can be True is upon
            environment termination/truncation).
        "done": A (batch, sequence) tensor of **cumulative** done conditions. For any given batch element,
            the first True is followed by True's all the way till the end. This can be used for masking
            extraneous elements from the sequences above.

    Args:
        env: The batch of environments.
        policy: The policy. Must be a PyTorch nn module.
        seeds: The environments are seeded once at the start of the rollout. If provided, this argument
            specifies the seeds for each of the environments.
        return_observations: Whether to include all observations in the returned rollout data. Observations
            are returned optionally because they typically take more memory to cache. Defaults to False.
        render_callback: Optional rendering callback to be used after the environments are reset, and after
            every step.
        enable_progbar: Enable a progress bar over rollout steps.
    Returns:
        The dictionary described above.
    """
    assert isinstance(policy, nn.Module), "Policy must be a PyTorch nn module."
    device = get_device_from_parameters(policy)

    # Reset the policy and environments.
    policy.reset()

    observation, info = env.reset(seed=seeds)
    if render_callback is not None:
        render_callback(env)

    all_observations = []
    all_actions = []
    all_rewards = []
    all_successes = []
    all_dones = []

    step = 0
    # Keep track of which environments are done.
    done = np.array([False] * env.num_envs)
    max_steps = env.call("_max_episode_steps")[0]
    progbar = trange(
        max_steps,
        desc=f"Running rollout with at most {max_steps} steps",
        disable=not enable_progbar,
        leave=False,
    )
    prior = None
    count = 0
    noise_count = 0
    while not np.all(done):
        # Numpy array to tensor and changing dictionary keys to LeRobot policy format.
        observation = preprocess_observation(observation)
        if return_observations:
            all_observations.append(deepcopy(observation))

        observation = {key: observation[key].to(device, non_blocking=True) for key in observation}  
        with torch.inference_mode():
            # action = policy.select_action(observation)
            if sampler == "coherence":
                action_dict = coherence_sampler(policy, prior, observation, count, ah_test, temperature=temperature)
            if sampler == "random":
                action_dict = random_sampler(policy, prior, observation, count, ah_test, temperature=temperature)
            if sampler == "contrastive":
                action_dict = contrastive_sampler(policy, reference_policy, prior, observation, count, ah_test, temperature=temperature)
            if sampler == "bidirectional":
                action_dict = bidirectional_sampler(policy, reference_policy, prior, observation, count, ah_test, temperature=temperature)
            action = action_dict['action']
            prior = action_dict['action_pred'][:, 1:, :] # update prior to not include the action that we are taking in this step


        action = action.squeeze(1)
        
        if ah_test > 1:
            if count == 0:
                if noise_level > 0.0:
                    noise_seed = (np.random.rand(action_dict['action_pred'].shape[0], 1, action_dict['action_pred'].shape[2]) + 0.5) * np.random.choice([-1, 1], size=(action_dict['action_pred'].shape[0], 1, action_dict['action_pred'].shape[2]))
                    action_step = (action_dict['action_pred'][:, 1:] - action_dict['action_pred'][:, :-1]).cpu().numpy()  # Convert to numpy
                    if action_step.shape[1] > 0:  # Check if the second dimension of action_step has a valid size
                        noise_step = noise_seed.repeat(action_step.shape[1], axis=1) * action_step
                        noise_cum = np.cumsum(noise_step, axis=1)
                        action = action + torch.from_numpy(noise_cum[:, 0, :]).to(action.device) * noise_level  # add noise to the current action
                        # Add noise to the prior actions
                        prior_noise = torch.from_numpy(noise_cum).to(prior.device) * noise_level
                        prior = prior + prior_noise
                    else:
                        # Add noise directly to the action
                        noise_direct = (np.random.rand(action.shape[0], action.shape[1]) - 0.5) * noise_level
                        action = action + torch.from_numpy(noise_direct).to(action.device)
        
        if ah_test == 1:
            if noise_count == 0:
                noise_seed = (np.random.rand(action_dict['action_pred'].shape[0], 1, action_dict['action_pred'].shape[2]) + 0.5) * np.random.choice([-1, 1], size=(action_dict['action_pred'].shape[0], 1, action_dict['action_pred'].shape[2]))
                action_step = (action_dict['action_pred'][:, 1:] - action_dict['action_pred'][:, :-1]).cpu().numpy()  # Convert to numpy
            if action_step.shape[1] > 0:  # Check if the second dimension of action_step has a valid size
                noise_step = noise_seed.repeat(action_step.shape[1], axis=1) * action_step
                noise_cum = np.cumsum(noise_step, axis=1)
                action = action + torch.from_numpy(noise_cum[:, 0, :]).to(action.device) * noise_level  # add noise to the current action
                # Add noise to the prior actions
                prior_noise = torch.from_numpy(noise_cum).to(prior.device) * noise_level
                prior = prior + prior_noise
            else:
                # Add noise directly to the action
                noise_direct = (np.random.rand(action.shape[0], action.shape[1]) - 0.5) * noise_level
                action = action + torch.from_numpy(noise_direct).to(action.device)
        action = action.to("cpu").numpy()
        assert action.ndim == 2, "Action dimensions should be (batch, action_dim)"

        # Apply the next action.
        observation, reward, terminated, truncated, info = env.step(action)
        if render_callback is not None:
            render_callback(env)

        # VectorEnv stores is_success in `info["final_info"][env_index]["is_success"]`. "final_info" isn't
        # available of none of the envs finished.
        if "final_info" in info:
            successes = [info["is_success"] if info is not None else False for info in info["final_info"]]
        else:
            successes = [False] * env.num_envs

        # Keep track of which environments are done so far.
        done = terminated | truncated | done

        all_actions.append(torch.from_numpy(action))
        all_rewards.append(torch.from_numpy(reward))
        all_dones.append(torch.from_numpy(done))
        all_successes.append(torch.tensor(successes))

        step += 1
        count = (count + 1) % ah_test
        noise_count = (noise_count + 1) % 5 # adding noise temporally correlated over 5 steps 
        running_success_rate = (
            einops.reduce(torch.stack(all_successes, dim=1), "b n -> b", "any").numpy().mean()
        )
        progbar.set_postfix({"running_success_rate": f"{running_success_rate.item() * 100:.1f}%"})
        progbar.update()

    # Track the final observation.
    if return_observations:
        observation = preprocess_observation(observation)
        all_observations.append(deepcopy(observation))

    # Stack the sequence along the first dimension so that we have (batch, sequence, *) tensors.
    ret = {
        "action": torch.stack(all_actions, dim=1),
        "reward": torch.stack(all_rewards, dim=1),
        "success": torch.stack(all_successes, dim=1),
        "done": torch.stack(all_dones, dim=1),
    }
    if return_observations:
        stacked_observations = {}
        for key in all_observations[0]:
            stacked_observations[key] = torch.stack([obs[key] for obs in all_observations], dim=1)
        ret["observation"] = stacked_observations

    return ret

def eval_policy(
    env: gym.vector.VectorEnv,
    policy: torch.nn.Module,
    n_episodes: int,
    max_episodes_rendered: int = 0,
    videos_dir: Path | None = None,
    return_episode_data: bool = False,
    start_seed: int | None = None,
    enable_progbar: bool = False,
    enable_inner_progbar: bool = False,
    sampler: str = "coherence", 
    ah_test: int = 1, 
    reference_policy: torch.nn.Module | None = None,
    temperature: float = 1.0,
    noise_level: float = 0.0
) -> dict:
    """
    Args:
        env: The batch of environments.
        policy: The policy.
        n_episodes: The number of episodes to evaluate.
        max_episodes_rendered: Maximum number of episodes to render into videos.
        videos_dir: Where to save rendered videos.
        return_episode_data: Whether to return episode data for online training. Incorporates the data into
            the "episodes" key of the returned dictionary.
        start_seed: The first seed to use for the first individual rollout. For all subsequent rollouts the
            seed is incremented by 1. If not provided, the environments are not manually seeded.
        enable_progbar: Enable progress bar over batches.
        enable_inner_progbar: Enable progress bar over steps in each batch.
    Returns:
        Dictionary with metrics and data regarding the rollouts.
    """
    if max_episodes_rendered > 0 and not videos_dir:
        raise ValueError("If max_episodes_rendered > 0, videos_dir must be provided.")

    assert isinstance(policy, Policy)
    start = time.time()
    policy.eval()

    # Determine how many batched rollouts we need to get n_episodes. Note that if n_episodes is not evenly
    # divisible by env.num_envs we end up discarding some data in the last batch.
    n_batches = n_episodes // env.num_envs + int((n_episodes % env.num_envs) != 0)

    # Keep track of some metrics.
    sum_rewards = []
    max_rewards = []
    all_successes = []
    all_seeds = []
    threads = []  # for video saving threads
    n_episodes_rendered = 0  # for saving the correct number of videos

    # Callback for visualization.
    def render_frame(env: gym.vector.VectorEnv):
        # noqa: B023
        if n_episodes_rendered >= max_episodes_rendered:
            return
        n_to_render_now = min(max_episodes_rendered - n_episodes_rendered, env.num_envs)
        if isinstance(env, gym.vector.SyncVectorEnv):
            ep_frames.append(np.stack([env.envs[i].render() for i in range(n_to_render_now)]))  # noqa: B023
        elif isinstance(env, gym.vector.AsyncVectorEnv):
            # Here we must render all frames and discard any we don't need.
            ep_frames.append(np.stack(env.call("render")[:n_to_render_now]))

    if max_episodes_rendered > 0:
        video_paths: list[str] = []

    if return_episode_data:
        episode_data: dict | None = None

    progbar = trange(n_batches, desc="Stepping through eval batches", disable=not enable_progbar)
    for batch_ix in progbar:
        # Cache frames for rendering videos. Each item will be (b, h, w, c), and the list indexes the rollout
        # step.
        if max_episodes_rendered > 0:
            ep_frames: list[np.ndarray] = []

        if start_seed is None:
            seeds = None
        else:
            seeds = range(
                start_seed + (batch_ix * env.num_envs), start_seed + ((batch_ix + 1) * env.num_envs)
            )
        rollout_data = rollout(
            env,
            policy,
            seeds=list(seeds) if seeds else None,
            return_observations=return_episode_data,
            render_callback=render_frame if max_episodes_rendered > 0 else None,
            enable_progbar=enable_inner_progbar,
            sampler=sampler, 
            ah_test=ah_test, 
            reference_policy=reference_policy,
            temperature=temperature,
            noise_level=noise_level,
        )

        # Figure out where in each rollout sequence the first done condition was encountered (results after
        # this won't be included).
        n_steps = rollout_data["done"].shape[1]
        # Note: this relies on a property of argmax: that it returns the first occurrence as a tiebreaker.
        done_indices = torch.argmax(rollout_data["done"].to(int), dim=1)

        # Make a mask with shape (batch, n_steps) to mask out rollout data after the first done
        # (batch-element-wise). Note the `done_indices + 1` to make sure to keep the data from the done step.
        mask = (torch.arange(n_steps) <= einops.repeat(done_indices + 1, "b -> b s", s=n_steps)).int()
        # Extend metrics.
        batch_sum_rewards = einops.reduce((rollout_data["reward"] * mask), "b n -> b", "sum")
        sum_rewards.extend(batch_sum_rewards.tolist())
        batch_max_rewards = einops.reduce((rollout_data["reward"] * mask), "b n -> b", "max")
        max_rewards.extend(batch_max_rewards.tolist())
        batch_successes = einops.reduce((rollout_data["success"] * mask), "b n -> b", "any")
        all_successes.extend(batch_successes.tolist())
        if seeds:
            all_seeds.extend(seeds)
        else:
            all_seeds.append(None)

        # FIXME: episode_data is either None or it doesn't exist
        if return_episode_data:
            this_episode_data = _compile_episode_data(
                rollout_data,
                done_indices,
                start_episode_index=batch_ix * env.num_envs,
                start_data_index=(0 if episode_data is None else (episode_data["index"][-1].item() + 1)),
                fps=env.unwrapped.metadata["render_fps"],
            )
            if episode_data is None:
                episode_data = this_episode_data
            else:
                # Some sanity checks to make sure we are correctly compiling the data.
                assert episode_data["episode_index"][-1] + 1 == this_episode_data["episode_index"][0]
                assert episode_data["index"][-1] + 1 == this_episode_data["index"][0]
                # Concatenate the episode data.
                episode_data = {k: torch.cat([episode_data[k], this_episode_data[k]]) for k in episode_data}

        # Maybe render video for visualization.
        if max_episodes_rendered > 0 and len(ep_frames) > 0:
            batch_stacked_frames = np.stack(ep_frames, axis=1)  # (b, t, *)
            for stacked_frames, done_index in zip(
                batch_stacked_frames, done_indices.flatten().tolist(), strict=False
            ):
                if n_episodes_rendered >= max_episodes_rendered:
                    break

                videos_dir.mkdir(parents=True, exist_ok=True)
                video_path = videos_dir / f"eval_episode_{n_episodes_rendered}.mp4"
                video_paths.append(str(video_path))
                thread = threading.Thread(
                    target=write_video,
                    args=(
                        str(video_path),
                        stacked_frames[: done_index + 1],  # + 1 to capture the last observation
                        env.unwrapped.metadata["render_fps"],
                    ),
                )
                thread.start()
                threads.append(thread)
                n_episodes_rendered += 1

        progbar.set_postfix(
            {"running_success_rate": f"{np.mean(all_successes[:n_episodes]).item() * 100:.1f}%"}
        )

    # Wait till all video rendering threads are done.
    for thread in threads:
        thread.join()

    # Compile eval info.
    info = {
        "per_episode": [
            {
                "episode_ix": i,
                "sum_reward": sum_reward,
                "max_reward": max_reward,
                "success": success,
                "seed": seed,
            }
            for i, (sum_reward, max_reward, success, seed) in enumerate(
                zip(
                    sum_rewards[:n_episodes],
                    max_rewards[:n_episodes],
                    all_successes[:n_episodes],
                    all_seeds[:n_episodes],
                    strict=True,
                )
            )
        ],
        "aggregated": {
            "avg_sum_reward": float(np.nanmean(sum_rewards[:n_episodes])),
            "avg_max_reward": float(np.nanmean(max_rewards[:n_episodes])),
            "pc_success": float(np.nanmean(all_successes[:n_episodes]) * 100),
            "eval_s": time.time() - start,
            "eval_ep_s": (time.time() - start) / n_episodes,
        },
    }

    if return_episode_data:
        info["episodes"] = episode_data

    if max_episodes_rendered > 0:
        info["video_paths"] = video_paths

    return info


def _compile_episode_data(
    rollout_data: dict, done_indices: Tensor, start_episode_index: int, start_data_index: int, fps: float
) -> dict:
    """Convenience function for `eval_policy(return_episode_data=True)`

    Compiles all the rollout data into a Hugging Face dataset.

    Similar logic is implemented when datasets are pushed to hub (see: `push_to_hub`).
    """
    ep_dicts = []
    total_frames = 0
    for ep_ix in range(rollout_data["action"].shape[0]):
        # + 2 to include the first done frame and the last observation frame.
        num_frames = done_indices[ep_ix].item() + 2
        total_frames += num_frames

        # Here we do `num_frames - 1` as we don't want to include the last observation frame just yet.
        ep_dict = {
            "action": rollout_data["action"][ep_ix, : num_frames - 1],
            "episode_index": torch.tensor([start_episode_index + ep_ix] * (num_frames - 1)),
            "frame_index": torch.arange(0, num_frames - 1, 1),
            "timestamp": torch.arange(0, num_frames - 1, 1) / fps,
            "next.done": rollout_data["done"][ep_ix, : num_frames - 1],
            "next.success": rollout_data["success"][ep_ix, : num_frames - 1],
            "next.reward": rollout_data["reward"][ep_ix, : num_frames - 1].type(torch.float32),
        }

        # For the last observation frame, all other keys will just be copy padded.
        for k in ep_dict:
            ep_dict[k] = torch.cat([ep_dict[k], ep_dict[k][-1:]])

        for key in rollout_data["observation"]:
            ep_dict[key] = rollout_data["observation"][key][ep_ix, :num_frames]

        ep_dicts.append(ep_dict)

    data_dict = {}
    for key in ep_dicts[0]:
        data_dict[key] = torch.cat([x[key] for x in ep_dicts])

    data_dict["index"] = torch.arange(start_data_index, start_data_index + total_frames, 1)

    return data_dict


def main(
    pretrained_policy_path: Path | None = None,
    hydra_cfg_path: str | None = None,
    out_dir: str | None = None,
    config_overrides: list[str] | None = None,
    ah_test: int = 1,
    reference_policy_path: Path | None = None,
    temperature: float = 1.0, 
    noise_level: float = 0.0
):
    assert (pretrained_policy_path is None) ^ (hydra_cfg_path is None)
    if pretrained_policy_path is not None:
        hydra_cfg = init_hydra_config(str(pretrained_policy_path / "config.yaml"), config_overrides)
    else:
        hydra_cfg = init_hydra_config(hydra_cfg_path, config_overrides)

    if out_dir is None:
        out_dir = f"outputs/eval/{dt.now().strftime('%Y-%m-%d/%H-%M-%S')}_{hydra_cfg.env.name}_{hydra_cfg.policy.name}"

    # Check device is available
    device = get_safe_torch_device(hydra_cfg.device, log=True)

    torch.backends.cudnn.benchmark = True
    torch.backends.cuda.matmul.allow_tf32 = True
    set_global_seed(hydra_cfg.seed)

    log_output_dir(out_dir)

    logging.info("Making environment.")
    env = make_env(hydra_cfg)

    logging.info("Making policy.")
    if hydra_cfg_path is None:
        policy = make_policy(hydra_cfg=hydra_cfg, pretrained_policy_name_or_path=str(pretrained_policy_path))
    else:
        # Note: We need the dataset stats to pass to the policy's normalization modules.
        policy = make_policy(hydra_cfg=hydra_cfg, dataset_stats=make_dataset(hydra_cfg).stats)

    assert isinstance(policy, nn.Module)
    policy.eval()

    policy.vqbet.action_head.config.bet_softmax_temperature = temperature

    # Load the reference policy if provided
    if reference_policy_path:
        reference_policy = make_policy(hydra_cfg=hydra_cfg, pretrained_policy_name_or_path=str(reference_policy_path))
        assert isinstance(reference_policy, nn.Module)
        reference_policy.eval()
    else:
        reference_policy = None

    with torch.no_grad(), torch.autocast(device_type=device.type) if hydra_cfg.use_amp else nullcontext():
        info = eval_policy(
            env,
            policy,
            hydra_cfg.eval.n_episodes,
            max_episodes_rendered=10,
            videos_dir=Path(out_dir) / "videos",
            start_seed=hydra_cfg.seed,
            enable_progbar=True,
            enable_inner_progbar=True,
            sampler=args.sampler, 
            ah_test=ah_test,  
            reference_policy=reference_policy,  
            temperature=temperature,
            noise_level=noise_level
        )
    print(info["aggregated"])

    # Save info
    with open(Path(out_dir) / "eval_info.json", "w") as f:
        json.dump(info, f, indent=2)

    env.close()

    logging.info("End of eval")


def get_pretrained_policy_path(pretrained_policy_name_or_path, revision=None):
    try:
        pretrained_policy_path = Path(snapshot_download(pretrained_policy_name_or_path, revision=revision))
    except (HFValidationError, RepositoryNotFoundError) as e:
        if isinstance(e, HFValidationError):
            error_message = (
                "The provided pretrained_policy_name_or_path is not a valid Hugging Face Hub repo ID."
            )
        else:
            error_message = (
                "The provided pretrained_policy_name_or_path was not found on the Hugging Face Hub."
            )

        logging.warning(f"{error_message} Treating it as a local directory.")
        pretrained_policy_path = Path(pretrained_policy_name_or_path)
    if not pretrained_policy_path.is_dir() or not pretrained_policy_path.exists():
        raise ValueError(
            "The provided pretrained_policy_name_or_path is not a valid/existing Hugging Face Hub "
            "repo ID, nor is it an existing local directory."
        )
    return pretrained_policy_path

if __name__ == "__main__":
    init_logging()

    parser = argparse.ArgumentParser(
        description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter
    )
    group = parser.add_mutually_exclusive_group(required=True)
    group.add_argument(
        "-p",
        "--pretrained-policy-name-or-path",
        help=(
            "Either the repo ID of a model hosted on the Hub or a path to a directory containing weights "
            "saved using `Policy.save_pretrained`. If not provided, the policy is initialized from scratch "
            "(useful for debugging). This argument is mutually exclusive with `--config`."
        ),
    )
    group.add_argument(
        "--config",
        help=(
            "Path to a yaml config you want to use for initializing a policy from scratch (useful for "
            "debugging). This argument is mutually exclusive with `--pretrained-policy-name-or-path` (`-p`)."
        ),
    )
    parser.add_argument("--revision", help="Optionally provide the Hugging Face Hub revision ID.")
    parser.add_argument(
        "--out-dir",
        help=(
            "Where to save the evaluation outputs. If not provided, outputs are saved in "
            "outputs/eval/{timestamp}_{env_name}_{policy_name}"
        ),
    )
    parser.add_argument(
        "overrides",
        nargs="*",
        help="Any key=value arguments to override config values (use dots for.nested=overrides)",
    )
    parser.add_argument(
        "--sampler",
        choices=["coherence", "random", "contrastive", "bidirectional"],
        default="bidirectional",
        help="Specify which sampler to use: coherence_sampler, random_sampler, contrastive_sampler or bidirectional_sampler.",
    )
    parser.add_argument(
        "--ah_test",
        type=int,
        default=1,
        help="Specify the ah_test value.",
    )
    parser.add_argument(
        "--reference-policy-name-or-path",
        help=(
            "Either the repo ID of a reference model hosted on the Hub or a path to a directory containing weights "
            "saved using `Policy.save_pretrained`. If not provided, no reference policy is used."
        ),
    )

    parser.add_argument(
        "--temperature",
        type=float,
        default=1.0,
        help="Specify the temperature value for VQBeTHead.",
    )

    parser.add_argument(
        "--noise_level",
        type=float,
        default=0.0,
        help="Specify the noise level to be added to actions.",
    )

    args = parser.parse_args()
    if args.pretrained_policy_name_or_path is None:
        main(
            hydra_cfg_path=args.config,
            out_dir=args.out_dir,
            config_overrides=args.overrides,
            ah_test=args.ah_test,
            reference_policy_path=args.reference_policy_name_or_path,
            temperature=args.temperature,
            noise_level=args.noise_level
        )
    else:
        pretrained_policy_path = get_pretrained_policy_path(
            args.pretrained_policy_name_or_path, revision=args.revision
        )

        main(
            pretrained_policy_path=pretrained_policy_path,
            out_dir=args.out_dir,
            config_overrides=args.overrides,
            ah_test=args.ah_test,
            reference_policy_path=args.reference_policy_name_or_path,
            temperature=args.temperature,
            noise_level=args.noise_level
        )

